The present invention relates generally to methods and systems for performing and evaluating mappings across multiple information models. More particularly, the present invention relates to methods and systems for forming clusters of elements in an information model, mapping elements of a cluster in one information model to elements of another information model, and evaluating the mappings of the clustered elements.
An information model is a way of representing and managing information, such as data, relationships, services, and processes, in data processing systems for a particular domain or enterprise. Every day, organizations deal with a myriad of different semantic expressions in key information, and expend huge resources working around the inconsistencies, challenges and errors introduced by so many varying information models. Examples of information models are Entity-Relationship (ER) models, Unified Modeling Language (UML) models, Eclipse Modeling Framework (EMF) models, thesauri, ontologies or Extensible Markup Language (XML) schema.
These varying models rarely share a common terminology, because they have emerged as a result of several inputs. In some cases, mergers of organizations operating in the same industry result in different information models to express the same exact concepts. In other cases, they may have been developed by different individuals to express overlapping industry concepts, but in slightly different domains.
Irrespective of the means through which these models came about, today's organizations utilize many different information models and face an increasing need to integrate across these models, through data integration, shared processes and rules, or reusable services. In all of these cases, the ability to relate, or map, between elements of different information models is a critical foundation stone in addressing these challenges.
A mapping between information models involves the matching of elements of the models, which may be based on, for example, lexical names, semantics, and/or other attributes. In integrating data across heterogeneous information models, mismatches in terminology and semantics across sources lead to laborious manual efforts to map.
Extensive research exists in determining how to automate or semi-automate mappings across many different types of information models. For example, schema mapping is a well-studied area for databases, as is ontology mapping (also called ontology alignment). Products such as IBM's IDA, FastTrack, and Discovery are capable of performing such functionalities. However, the existing research and products are geared towards large sets of field-by-field or element-by-element mappings.